Triple
T13003496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saniyya Sidney |
E322226
|
entity |
| Predicate | appearsIn |
P795
|
FINISHED |
| Object | Fast Color |
E1016087
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Fast Color | Statement: [Saniyya Sidney, appearsIn, Fast Color]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fast Color Context triple: [Saniyya Sidney, appearsIn, Fast Color]
-
A.
Fast Color
chosen
Fast Color is a 2018 science-fiction drama film about a woman with supernatural powers who returns home to confront her past and her family's legacy.
-
B.
Fast
"Fast" is a country song by Luke Bryan that reflects on the fleeting nature of time, relationships, and life’s milestones.
-
C.
Fast
Fast is a surname most notably associated with American novelist and screenwriter Howard Fast, known for his historical and political works.
-
D.
Fastiv
Fastiv is a historic city in northern Ukraine known as a regional railway hub and industrial center southwest of Kyiv.
-
E.
FAST
FAST is a U.S. federal law that authorizes long-term funding and policy for the nation’s surface transportation infrastructure, including highways, transit, and rail.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e9a2a448190968833354280e474 |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbc5c9a88190b70bda472bf3b062 |
completed | May 3, 2026, 4:15 a.m. |
Created at: April 9, 2026, 8:47 p.m.